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We present MobIE, a German-language dataset, which is human-annotated with 20coarse- and fine-grained entity types and entity linking information forgeographically linkable entities. The dataset consists of 3,232 social mediatexts and traffic reports with 91K tokens, and contains 20.5K annotatedentities, 13.1K of which are linked to a knowledge base. A subset of thedataset is human-annotated with seven mobility-related, n-ary relation types,while the remaining documents are annotated using a weakly-supervised labelingapproach implemented with the Snorkel framework. To the best of our knowledge,this is the first German-language dataset that combines annotations for NER, ELand RE, and thus can be used for joint and multi-task learning of thesefundamental information extraction tasks. We make MobIE public athttps://github.com/dfki-nlp/mobie.

MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain